Automatic Service Categorisation through Machine Learning in Emergent Middleware

Amel Bennaceur (1) , Valérie Issarny (1) , Johansson Richard (2) , Moschitti Alessandro (3) , Spalazzese Romina (4) , Daniel Sykes (1)

1 ARLES - Software architectures and distributed systems
2 Department of Swedish [Gothenburg]
3 DISI - Information Engineering and Computer Science Department
4 DI - Dipartimento di Informatica [Italy]

The modern environment of mobile, pervasive, evolving ser- vices presents a great challenge to traditional solutions for enabling in- teroperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To over- come this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface descrip- tion. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmak- ing between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project.

Domaines: Informatique ubiquitaire

Amel Bennaceur, Valérie Issarny, Johansson Richard, Moschitti Alessandro, Spalazzese Romina, et al.. Automatic Service Categorisation through Machine Learning in Emergent Middleware. FMCO 2011 - 10th International Symposium on Formal Methods for Components and Objects, Oct 2011, Turin, Italy. pp.133-149, ⟨10.1007/978-3-642-35887-6_7⟩. ⟨hal-00718655⟩